Data analysis in each problem should contain two parts. One part should be aimed on a statistician who is familiar with linear models. In this part you should explain in detail what you have done and why. Give names of the techniques used, you may refer to the course lecture notes. Plots, diagrams often help. You must use Minitab for data analysis. Include the most relevant, in your opinion, parts of your Minitab work session into the report, and if you do so, then refer directly to the included Minitab results when making any conclusions or assumptions. Minitab output included without any explanation or comments will not be marked. The other part of a problem analysis should be aimed on a person who has some idea of basic statistics. In this part you should, without going into technicalities, explain briefly what you have done. In other words, provide a summary of your statistical analysis, accessible for general public. A lot of marks are given for the quality of the discussion and the analysis, clarity and readability of the project. Your project should be no more than ten pages long in total (including the cover sheet), longer projects will be penalized.
Problem 1. 32 measurements of a response variable Y and a predictor variable X are given in file PROBLEM1-DATA.MTW on Moodle (Week 7, Data for Problem 1). Plot Y
against X in a scatter plot to get an impression of the possible dependence. Is it reasonable
to use a simple linear regression model for modelling dependence of Y on X? Propose a
Polynomial model (by using Minitab), which explains the observed dependence. Justify your choice of the model.
Problem 2. A pharmaceutical company would like to know how a certain variable of
interest (response variable) can be explained by six potentially useful predictor variables.
30 measurements of predictors and the corresponding values of the response variable are
summarized in PROBLEM2-DATA.MTW on Moodle (Week 7, Data for Problem 2). The
response variable is denoted by Y and six predictors are denoted by X1;X2;X3;X4;X5
and X6 respectively. Your task is to analyze these data (by using Minitab) and propose a linear regression model which, in your opinion, explains variability of the response variable
most effectively. Justify your choice of the model.
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